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 Computer Science , 2012, Abstract: The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.
 International Journal of Computer Science Issues , 2011, Abstract: The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer system devised to support a radiologist in detection MCCs in digital mammography has been proposed. First, to facilitate and improve detection step, the mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and Watershed segmentation. The purpose of this paper is to find out which segmentation method is more appropriate for extracting suspicious areas that contain MCCs in mammograms. Finally the MCCs detection areas in different algorithms will be compared.
 Computer Science , 2014, Abstract: In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast masses from mammograms. We also propose an easily reproducible quantitative analysis to as- sess the performance of breast mass segmentation methodologies based on widely accepted accuracy and running time measurements on public datasets, which will facilitate further comparisons for this segmentation problem. In particular, we use two publicly available datasets (DDSM-BCRP and INbreast) and propose the computa- tion of the running time taken for the methodology to produce a mass segmentation given an input image and the use of the Dice index to quantitatively measure the segmentation accuracy. For both databases, we show that our proposed methodology produces competitive results in terms of accuracy and running time.
 Folia Histochemica et Cytobiologica , 2010, DOI: 10.5603/4320 Abstract: Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.
 International Journal of Advanced Computer Sciences and Applications , 2012, Abstract: The mammography is the most effective procedure for an early diagnosis of the breast cancer. Finding an accurate and efficient breast region segmentation technique still remains a challenging problem in digital mammography. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing algorithm in order to: remove digitization noises and separate background region from the breast profile region for further edge detection and regions segmentation.
 Gejza Jen？a Mathematics , 2015, DOI: 10.1017/S0004972700019705 Abstract: Effect algebras, introduced by Foulis and Bennett in 1994, are partial algebras which generalize some well known classes of algebraic structures (for example orthomodular lattices, MV algebras, orthoalgebras etc.). In the present paper, we introduce a new class of effect algebras, called {\em homogeneous effect algebras}. This class includes orthoalgebras, lattice ordered effect algebras and effect algebras satisfying Riesz decomposition property. We prove that every homogeneous effect algebra is a union of its blocks, which we define as maximal sub-effect algebras satisfying Riesz decomposition property. This generalizes a recent result by Rie\v{c}anov\'a, in which lattice ordered effect algebras were considered. Moreover, the notion of a block of a homogeneous effect algebra is a generalization of the notion of a block of an orthoalgebra. We prove that the set of all sharp elements in a homogeneous effect algebra $E$ forms an orthoalgebra $E_S$. Every block of $E_S$ is the center of a block of $E$. The set of all sharp elements in the compatibility center of $E$ coincides with the center of $E$. Finally, we present some examples of homogeneous effect algebras and we prove that for a Hilbert space $\mathbb H$ with $dim(\mathbb H)>1$, the standard effect algebra $\mathcal E(\mathbb H)$ of all effects in $\mathbb H$ is not homogeneous.
 Computer Science , 2013, Abstract: Breast tissue segmentation into dense and fat tissue is important for determining the breast density in mammograms. Knowing the breast density is important both in diagnostic and computer-aided detection applications. There are many different ways to express the density of a breast and good quality segmentation should provide the possibility to perform accurate classification no matter which classification rule is being used. Knowing the right breast density and having the knowledge of changes in the breast density could give a hint of a process which started to happen within a patient. Mammograms generally suffer from a problem of different tissue overlapping which results in the possibility of inaccurate detection of tissue types. Fibroglandular tissue presents rather high attenuation of X-rays and is visible as brighter in the resulting image but overlapping fibrous tissue and blood vessels could easily be replaced with fibroglandular tissue in automatic segmentation algorithms. Small blood vessels and microcalcifications are also shown as bright objects with similar intensities as dense tissue but do have some properties which makes possible to suppress them from the final results. In this paper we try to divide dense and fat tissue by suppressing the scattered structures which do not represent glandular or dense tissue in order to divide mammograms more accurately in the two major tissue types. For suppressing blood vessels and microcalcifications we have used Gabor filters of different size and orientation and a combination of morphological operations on filtered image with enhanced contrast.
 International Journal of Recent Technology and Engineering , 2013, Abstract: Breast cancer continues to be one of the major causes of death among women. Early detection is a key factor to the success of treatment process. X-ray mammography is one of the most common procedures for diagnosing breast cancer due to its simplicity, portability and cost effectiveness. Mass detection using Computer Aided Diagnosis (CAD) schemes was an active field of research in the past few years, and some of these studies showed a promising future. T`hese CAD systems serve as a second decision tool to radiologists for discovering masses in the mammograms. In this paper, a breast mass segmentation method is presented based on adaptive median filtering and texture analysis. The algorithm is implemented using MATLAB environment. The program accepts a digital mammographic image (images taken from the Mammographic Image Analysis Society (MIAS) database). Adaptive median filtering is applied for contouring the image, then the best contour is chosen based on the texture properties of the resulting Region-of-Interest (ROI). The proposed CAD system produces (92.307%) mass sensitivity at 2.75 False Positive per Image (FPI) which is considered as a proper result in this field of research.
 International Journal of Image, Graphics and Signal Processing , 2009, Abstract: In this paper we propose a color image segmentation algorithm based on perceptual color vision model. First, the original image is divide into image blocks which are not overlapped; then, the mean and variance of every image back was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks and texture blocks by the variance of it. The initial seed regions are automatically selected depending on calculating the homogeneous color blocks' color difference in CIEL*a*b* color space and spatial information. The color contrast gradient of the texture blocks need to calculate and the edge information are stored for regional growing. The fuzzy region growing algorithm and coloredge detection to obtain a final segmentation map. The experimental segmentation results hold favorable consistency in terms of human perception, and confirm effectiveness of the algorithm.
 Natural Science (NS) , 2015, DOI: 10.4236/ns.2015.73014 Abstract: The article presents the results of the experimental study that was devoted to determining the blocking influence of the solar microwave radiation on the process of biofilm formation in Gram-positive and Gram-negative microorganisms. The microwave generator that allows simulating microwave “splashes” of the Sun in the frequency range (4.0 - 4.3 GHz) with the controlled intensity of radiation (from 50 μW/sm2 to 500 μW/sm2) was used for conducting this research. It is found out that the simulated solar radiation of the microwave range blocks the formation of the extracellular matrix by the opportunistic microorganisms. The results of this study confirm the hypothesis of the evolutionary nature of the leading role of the microwave radiation of the Sun in the life processes of organisms. The technology of the exposure on the microorganisms that was used in the experiment opens up the real prospects for reducing the persistent potential of microorganisms and improving the efficiency of the bacterial infections treatment.
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